Results 1  10
of
380,370
Regression Framework: Further Evidence
"... The paper investigates the validity of PPP by using 15 OECD countries data of monthly frequency from 1980:01 to 2005:12 and tests for the symmetry and proportionality hypotheses. The test for PPP is conducted in the framework of the General Relative PPP (RPPP) as proposed by Coakley et al. (2005) us ..."
Abstract
 Add to MetaCart
The paper investigates the validity of PPP by using 15 OECD countries data of monthly frequency from 1980:01 to 2005:12 and tests for the symmetry and proportionality hypotheses. The test for PPP is conducted in the framework of the General Relative PPP (RPPP) as proposed by Coakley et al. (2005
Least angle regression
 Ann. Statist
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
Abstract

Cited by 1308 (43 self)
 Add to MetaCart
to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
Predictive regressions
 Journal of Financial Economics
, 1999
"... When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression set ..."
Abstract

Cited by 452 (19 self)
 Add to MetaCart
When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression
Very Robust Regression: Frameworks for Comparisons
"... We use a smoothly parameterized series of examples that shows, in a systematic way, how the behaviour of algorithms for very robust regression depends on the closeness of the outliers to the main data. An algorithm based on the Forward Search outperforms Least Trimmed Squares and its reweighted vers ..."
Abstract
 Add to MetaCart
We use a smoothly parameterized series of examples that shows, in a systematic way, how the behaviour of algorithms for very robust regression depends on the closeness of the outliers to the main data. An algorithm based on the Forward Search outperforms Least Trimmed Squares and its reweighted
Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants
 Review of Economic Studies
, 2002
"... This paper empirically investigates the effects of liberalized trade on plant productivity in the case of Chile. Chile presents an interesting setting to study this relationship since it underwent a massive trade liberalization that significantly exposed its plants to competition from abroad during ..."
Abstract

Cited by 530 (14 self)
 Add to MetaCart
on consistent estimates of the input coefficients. In the second step, I identify the impact of trade on plantsâ€™ productivity in a regression framework allowing variation in productivity over time and across tradedand nontradedgoods sectors. Using plantlevel panel data on Chilean manufacturers, I find
A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized tTest and Statistical Inferences of Gene Changes
 Bioinformatics
, 2001
"... Motivation: DNA microarrays are now capable of providing genomewide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory ..."
Abstract

Cited by 485 (6 self)
 Add to MetaCart
due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model logexpression values by independent normal
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
Abstract

Cited by 958 (5 self)
 Add to MetaCart
This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
Abstract

Cited by 560 (20 self)
 Add to MetaCart
We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class
Results 1  10
of
380,370